No Cover Image

Journal article 411 views

The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis

Nicolás Rubido, Venia Batziou, Marwan Fuad, Vesna Vuksanovic Orcid Logo

arXiv

Swansea University Authors: Venia Batziou, Vesna Vuksanovic Orcid Logo

Full text not available from this repository: check for access using links below.

DOI (Published version): 10.48550/arXiv.2505.03458

Abstract

Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal inf...

Full description

Published in: arXiv
Published:
URI: https://cronfa.swan.ac.uk/Record/cronfa69526
first_indexed 2025-05-16T09:52:23Z
last_indexed 2025-12-05T17:56:56Z
id cronfa69526
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-12-04T14:16:24.8265837</datestamp><bib-version>v2</bib-version><id>69526</id><entry>2025-05-16</entry><title>The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis</title><swanseaauthors><author><sid>35a4b3e1c71f435e8dfdf2a35f442eaa</sid><firstname>Venia</firstname><surname>Batziou</surname><name>Venia Batziou</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a1a6e2bd0b6ee99f648abb6201dea474</sid><ORCID>0000-0003-4655-698X</ORCID><firstname>Vesna</firstname><surname>Vuksanovic</surname><name>Vesna Vuksanovic</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-05-16</date><abstract>Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum.</abstract><type>Journal Article</type><journal>arXiv</journal><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords/><publishedDay>0</publishedDay><publishedMonth>0</publishedMonth><publishedYear>0</publishedYear><publishedDate>0001-01-01</publishedDate><doi>10.48550/arXiv.2505.03458</doi><url/><notes>Preprint article before certification by peer review.</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2025-12-04T14:16:24.8265837</lastEdited><Created>2025-05-16T10:50:20.4610432</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Nicol&#xE1;s</firstname><surname>Rubido</surname><order>1</order></author><author><firstname>Venia</firstname><surname>Batziou</surname><order>2</order></author><author><firstname>Marwan</firstname><surname>Fuad</surname><order>3</order></author><author><firstname>Vesna</firstname><surname>Vuksanovic</surname><orcid>0000-0003-4655-698X</orcid><order>4</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2025-12-04T14:16:24.8265837 v2 69526 2025-05-16 The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis 35a4b3e1c71f435e8dfdf2a35f442eaa Venia Batziou Venia Batziou true false a1a6e2bd0b6ee99f648abb6201dea474 0000-0003-4655-698X Vesna Vuksanovic Vesna Vuksanovic true false 2025-05-16 Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum. Journal Article arXiv 0 0 0 0001-01-01 10.48550/arXiv.2505.03458 Preprint article before certification by peer review. COLLEGE NANME COLLEGE CODE Swansea University 2025-12-04T14:16:24.8265837 2025-05-16T10:50:20.4610432 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Nicolás Rubido 1 Venia Batziou 2 Marwan Fuad 3 Vesna Vuksanovic 0000-0003-4655-698X 4
title The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
spellingShingle The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
Venia Batziou
Vesna Vuksanovic
title_short The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
title_full The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
title_fullStr The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
title_full_unstemmed The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
title_sort The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
author_id_str_mv 35a4b3e1c71f435e8dfdf2a35f442eaa
a1a6e2bd0b6ee99f648abb6201dea474
author_id_fullname_str_mv 35a4b3e1c71f435e8dfdf2a35f442eaa_***_Venia Batziou
a1a6e2bd0b6ee99f648abb6201dea474_***_Vesna Vuksanovic
author Venia Batziou
Vesna Vuksanovic
author2 Nicolás Rubido
Venia Batziou
Marwan Fuad
Vesna Vuksanovic
format Journal article
container_title arXiv
institution Swansea University
doi_str_mv 10.48550/arXiv.2505.03458
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 0
active_str 0
description Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum.
published_date 0001-01-01T05:28:25Z
_version_ 1851097880292491264
score 11.089407

Similar Items